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In order to characterize thermal dependent physical properties of materials, potentially to be used in technological applications, an accurate interatomic-potential parameter set is a must. In general, conjugate-gradient methods and more recently, metaheuristics such as genetic algorithms are employed in determining these interatomic potentials, however, especially the use of metaheuristics specifically designed for optimization of real valued problems such as particle swarm and evaluation strategies are limited in the mentioned problem. In addition, some of these parameters are conflicting in nature, for which multi objective optimization procedures have a great potential for better understanding of these conflicts. In this respect, we aim to present a widely used interatomic potential parameter set, the Stillinger–Weber potential, obtained through three different optimization methods (particle swarm optimization, PSO, covariance matrix adaptation evolution strategies, CMA-ES, and non-dominated sorting genetic algorithm, NSGA-III) for two-dimensional materials MoS2, WS2, WSe2, and MoSe2. These two-dimensional transition metal dichalcogenides are considered as a case mainly due to their potential in a variety of promising technologies for next generation flexible and low-power nanoelectronics, (such as photonics, valleytronics, sensing, energy storage, and optoelectronic devices) as well as their excellent physical properties (such as electrical, mechanical, thermal, and optical properties) different from those of their bulk counterparts. The results show that the outputs of all optimization methods converge to ideal values with sufficiently long iterations and at different trials. However, when we consider the results of the statistical analyses of different trials under similar conditions, we observe that the method with the lowest error rate is the CMA-ES.

Efficient thermal transport control is a fundamental issue for electronic device applications such as information, communication, and energy storage technologies in modern electronics in order to achieve desired thermal conditions. Structural defects in materials provide a mechanism to adjust the thermal transport properties of these materials on demand. In this context, the effect of structural defects on lattice thermal conductivities of two-dimensional hexagonal binary group-III nitride (XN, X = B, Al, and Ga) semiconductors is systematically investigated by means of classical molecular dynamics simulations performed with recently developed transferable inter-atomic potentials accurately describing defect energies. Here, two different Green-Kubo based approaches and another approach based on non-equilibrium molecular dynamics are compared in order to get an overall understanding. Our investigation clearly shows that defect concentrations of 3% decrease the thermal conductivity of systems containing these nitrites up to 95%. Results hint that structural defects can be used as effective adjustment parameters in controlling thermal transport properties in device applications associated with these materials. Published under an exclusive license by AIP Publishing.

Direct experimental measurement of thermal expansion coefficient without substrate effects is a challenging task for two-dimensional (2D) materials, and its accurate estimation with large-scale ab initio molecular dynamics is computationally very expensive. Machine learning-based interatomic potentials trained with ab initio data have been successfully used in molecular dynamics simulations to decrease the computational cost without compromising the accuracy. In this study, we investigated using Gaussian approximation potentials to reproduce the density functional theory-level accuracy for graphene within both lattice dynamical and molecular dynamical methods, and to extend their applicability to larger length and time scales. Two such potentials are considered, GAP17 and GAP20. GAP17, which was trained with pristine graphene structures, is found to give closer results to density functional theory calculations at different scales. Further vibrational and structural analyses verify that the same conclusions can be deduced with density functional theory level in terms of the reasoning of the thermal expansion behavior, and the negative thermal expansion behavior is associated with long-range out-of-plane phonon vibrations. Thus, it is argued that the enabled larger system sizes by machine learning potentials may even enhance the accuracy compared to small-size-limited ab initio molecular dynamics.

The structure and thermal boundary conductance of the wurtzite GaN/AlN (0001) interface are investigated using molecular dynamics simulation. Simulation results with three different empirical interatomic potentials have produced similar misfit dislocation networks and dislocation core structures. Specifically, the misfit dislocation network at the GaN/AlN interface is found to consist of pure edge dislocations with a Burgers vector of 1/3(1 (2) over bar 10) and the misfit dislocation core has an eight-atom ring structure. Although different interatomic potentials lead to different dislocation properties and thermal conductance values, all have demonstrated a significant effect of misfit dislocations on the thermal boundary conductance of the GaN/AlN (0001) interface. Published under an exclusive license by AIP Publishing.

We present here a new interatomic potential parameter set to predict the thermal conductivity of zirconium trisulfide monolayers. The generated Tersoff-type force field is parameterized using data collected with first-principles calculations. We use non-equilibrium molecular dynamics simulations to predict the thermal conductivity. The generated parameters result in very good agreement in structural, mechanical, and dynamical parameters. The room temperature lattice thermal conductivity (κ) of the considered crystal is predicted to be κxx = 25.69 W m−1 K−1 and κyy = 42.38 W m−1 K−1, which both agree well with their corresponding first-principles values with a discrepancy of less than 5%. Moreover, the calculated κ variation with temperature (200 and 400 K) are comparable within the framework of the accuracy of both first-principles and molecular dynamics simulations, We acknowledge the financial support received from the European Union’s Horizon 2020 Research and Innovation Program under Grant Agreement No. 793726 (TELIOTES—Thermal and electronic transport in inorganic–organic thermoelectric superlattices) and the support of the Supercomputing Center of Galicia (CESGA), where the calculations have been made. We also acknowledge financial support by the Ministerio de Economía, Industria y Competitividad (MINECO) under Grant No. FEDER-MAT2017-90024-P, the Severo Ochoa Centres of Excellence Program under Grant No. SEV-2015-0496, and the Generalitat de Catalunya under Grant No. 2017 SGR 1506. The authors acknowledge the fruitful discussions with Dr. Jesús Carrete Montaña at TU Wien.

In this study, by means of classical molecular dynamics simulations, we investigate the thermal-transport properties of hexagonal single-layer, zinc-blend, and wurtzite phases of $\mathrm{BN}$, $\mathrm{Al}\mathrm{N}$, and $\mathrm{Ga}\mathrm{N}$ crystals, which are very promising for the application and design of high-quality electronic devices. With this in mind, we generate fully transferable Tersoff-type empirical interatomic potential parameter sets by utilizing an optimization procedure based on particle-swarm optimization. The predicted thermal properties as well as the structural, mechanical, and vibrational properties of all materials are in very good agreement with existing experimental and first-principles data. The impact of isotopes on thermal transport is also investigated and between approximately $10$ and 50% reduction in phonon thermal transport with random isotope distribution is observed in $\mathrm{BN}$ and $\mathrm{Ga}\mathrm{N}$ crystals. Our investigation distinctly shows that the generated parameter sets are fully transferable and very useful in exploring the thermal properties of systems containing these nitrides.